Skip to main content

Advertisement

SpringerLink
Log in

Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach

  • Original Paper
  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

In developed countries, colorectal cancer is the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy is often required to determine the need for second-line chemotherapy and eligibility for surgery. However, while FOLFOX-based regimens are typically used for CLM treatment, the identification of responsive patients remains elusive. Computer-aided diagnosis systems may provide insight in the classification of liver metastases identified on diagnostic images. In this paper, we propose a fully automated framework based on deep convolutional neural networks (DCNN) which first differentiates treated and untreated lesions to identify new lesions appearing on CT scans, followed by a fully connected neural networks to predict from untreated lesions in pre-treatment computed tomography (CT) for patients with CLM undergoing chemotherapy, their response to a FOLFOX with Bevacizumab regimen as first-line of treatment. The ground truth for assessment of treatment response was histopathology-determined tumor regression grade. Our DCNN approach trained on 444 lesions from 202 patients achieved accuracies of 91% for differentiating treated and untreated lesions, and 78% for predicting the response to FOLFOX-based chemotherapy regimen. Experimental results showed that our method outperformed traditional machine learning algorithms and may allow for the early detection of non-responsive patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. American Cancer Society Key Statistics for Colorectal Cancer. Available at https://www.cancer.org/cancer/colon-rectal-cancer/about/key-statistics.html. Accessed 24 January 2019.

  2. Massmann A, Rodt T, Marquardt S, Seidel R, Thomas K, Wacker F, Richter GM, Kauczor HU, Bücker A, Pereira PL, Sommer CM: Transarterial chemoembolization (TACE) for colorectal liver metastases—current status and critical review. Langenbeck's Arch Surg 400:641–659, 2015

    Article  Google Scholar 

  3. Thibodeau-Antonacci A, Petitclerc L, Gilbert G, Bilodeau L, Olivié D, Cerny M, Castel H, Turcotte S, Huet C, Perreault P, Soulez G, Chagnon M, Kadoury S, Tang A: Dynamic contrast-enhanced MRI to assess hepatocellular carcinoma response to Transarterial chemoembolization using LI-RADS criteria: A pilot study. Magn Reson Imaging, 2019. https://doi.org/10.1016/j.mri.2019.06.017

  4. Bonanni L, Carino NDL, Deshpande R, Ammori BJ, Sherlock DJ, Valle JW, Tam E, O’Reilly DA: A comparison of diagnostic imaging modalities for colorectal liver metastases. Eur J Surg Oncol 40(5):545–550, 2014

    Article  CAS  Google Scholar 

  5. Loupakis F, Schirripa M, Caparello C, Funel N, Pollina L, Vasile E, Cremolini C, Salvatore L, Morvillo M, Antoniotti C, Marmorino F, Masi G, Falcone A: Histopathologic evaluation of liver metastases from colorectal cancer in patients treated with FOLFOXIRI plus bevacizumab. Br J Cancer 108:2549–2556, 2013. https://doi.org/10.1038/bjc.2013.245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H: Brain tumor segmentation with Deep Neural Networks. Med Image Anal 35:18–31, 2017. https://doi.org/10.1016/j.media.2016.05.004

    Article  PubMed  Google Scholar 

  7. Simpson AL, Doussot A, Creasy JM, Adams LB, Allen PJ, DeMatteo RP, Gönen M, Kemeny NE, Kingham TP, Shia J, Jarnagin WR, Do RKG, D’Angelica MI: Computed Tomography Image Texture: A Noninvasive Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic Colorectal Cancer. Ann Surg Oncol 24:2482–2490, 2017. https://doi.org/10.1245/s10434-017-5896-1

    Article  PubMed  PubMed Central  Google Scholar 

  8. Haralick RM, Shanmugam K, Dinstein I: Textural Features for Image Classification. IEEE Trans Syst Man Cybern SMC-3:610–621, 2007. https://doi.org/10.1109/tsmc.1973.4309314

    Article  Google Scholar 

  9. Ba-Ssalamah A, Muin D, Schernthaner R, Kulinna-Cosentini C, Bastati N, Stift J, Gore R, Mayerhoefer ME: Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol 82, 2013. https://doi.org/10.1016/j.ejrad.2013.06.024

  10. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V: Assessment of Primary Colorectal Cancer Heterogeneity by Using Whole-Tumor Texture Analysis: Contrast-enhanced CT Texture as a Biomarker of 5-year Survival. Radiology 266:177–184, 2012. https://doi.org/10.1148/radiol.12120254

    Article  PubMed  Google Scholar 

  11. Hayano K, Yoshida H, Zhu AX, Sahani DV: Fractal analysis of contrast-enhanced CT images to predict survival of patients with hepatocellular carcinoma treated with sunitinib. Dig Dis Sci 59:1996–2003, 2014. https://doi.org/10.1007/s10620-014-3064-z

    Article  CAS  PubMed  Google Scholar 

  12. Lubner MG, Stabo N, Lubner SJ, del Rio AM, Song C, Halberg RB, Pickhardt PJ: CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging 40:2331–2337, 2015. https://doi.org/10.1007/s00261-015-0438-4

    Article  PubMed  Google Scholar 

  13. Ganeshan B, Miles KA, Young RCD, Chatwin CR: In Search of Biologic Correlates for Liver Texture on Portal-Phase CT. Acad Radiol 14:1058–1068, 2007. https://doi.org/10.1016/j.acra.2007.05.023

    Article  PubMed  Google Scholar 

  14. Miles KA, Ganeshan B, Griffiths MR, Young RCD, Chatwin CR: Colorectal Cancer: Texture Analysis of Portal Phase Hepatic CT Images as a Potential Marker of Survival. Radiology 250:444–452, 2009. https://doi.org/10.1148/radiol.2502071879

    Article  PubMed  Google Scholar 

  15. Bengio Y, Courville A, Vincent P: Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35:1798–1828, 2013. https://doi.org/10.1109/TPAMI.2013.50

    Article  PubMed  Google Scholar 

  16. Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E: Deep Learning for Computer Vision: A Brief Review. Comput Intell Neurosci 2018:1–13, 2018. https://doi.org/10.1155/2018/7068349

    Article  Google Scholar 

  17. Gladis VP, Rathi P, Palani S: Brain Tumor Detection and Classification Using Deep Learning Classifier on MRI Images 1. Res J Appl Sci Eng Technol 10:177–187, 2015

    Google Scholar 

  18. Paul R, Hawkins SH, Hall LO, Goldgof DB, Gillies RJ: Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 2017, pp 2570–2575

  19. Nie D, Zhang H, Adeli E, Liu L, Shen D: 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, pp 212–220.

  20. Kumar N, Verma R, Arora A, Kumar A, Gupta S, Sethi A, Gann PH: Convolutional neural networks for prostate cancer recurrence prediction. In: Medical Imaging 2017: Digital Pathology, 2017, p. 101400H

    Google Scholar 

  21. Chang C-C, Lin C-J: LIBSVM. ACM Trans Intell Syst Technol 2:1–27, 2011. https://doi.org/10.1145/1961189.1961199

    Article  Google Scholar 

  22. LeCun Y, Bengio Y, Hinton G: Deep learning. Nature 521:436–444, 2015

    Article  CAS  Google Scholar 

  23. Schmidhuber J: Deep Learning in neural networks: An overview. Neural Netw 61:85–117, 2015

    Article  Google Scholar 

  24. Bibault JE, Giraud P, Durdux C, Taieb J, Berger A, Coriat R, Chaussade S, Dousset B, Nordlinger B, Burgun A: Deep learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep 8:12611, 2018. https://doi.org/10.1038/s41598-018-30657-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vorontsov E, Tang A, Pal C, Kadoury S: Liver lesion segmentation informed by joint liver segmentation. In: Proceedings - International Symposium on Biomedical Imaging, 2018, pp 1332–1335

  26. Rubbia-Brandt L, Giostra E, Brezault C, Roth AD, Andres A, Audard V, Sartoretti P, Dousset B, Majno PE, Soubrane O, Chaussade S, Mentha G, Terris B: Importance of histological tumor response assessment in predicting the outcome in patients with colorectal liver metastases treated with neo-adjuvant chemotherapy followed by liver surgery. Ann Oncol 18:299–304, 2007. https://doi.org/10.1093/annonc/mdl386

    Article  CAS  PubMed  Google Scholar 

  27. Rodel C, Martus P, Papadoupolos T, Füzesi L, Klimpfinger M, Fietkau R, Wittekind C: Prognostic significance of tumor regression after preoperative chemoradiotherapy for rectal cancer. J Clin Oncol 23(34):8688–8696, 2005

    Article  Google Scholar 

  28. Chollet F, et al: Keras. 2015. Retrieved from https://github.com/fchollet/keras

  29. Coelho LP: Mahotas: Open source software for scriptable computer vision. J Open Res Softw 1(1):e3, 2013. https://doi.org/10.5334/jors.ac

    Article  Google Scholar 

  30. Pedregosa F, et al: Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830, 2011

    Google Scholar 

  31. Ravichandran K, Braman N, Janowczyk A, Madabhushi A: A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI. In: Medical Imaging 2018: Computer-Aided Diagnosis, Vol. 10575, 2018, p. 105750C

    Google Scholar 

Download references

Acknowledgments

We would like to express our appreciation to Imagia Cybernetics for providing the desired hardware for doing our experiments.

Funding

This study was financially supported by MEDTEQ and IVADO grants, as well as the MITACS organization. The clinical and radiological data was provided by Centre de recherche du Centre hospitalier de l’Université de Montréal (CR-CHUM). The Fonds de recherche du Québec en Santé and Fondation de l’association des radiologistes du Québec (FRQ-S and FARQ no. 34939) financially supported An Tang.

Author information

Authors and Affiliations

  1. Polytechnique Montréal, Montreal, QC, Canada

    Ahmad Maaref, Francisco Perdigon Romero & Samuel Kadoury

  2. Centre de recherche du Centre hospitalier de l’Université de Montréal (CRCHUM), Montreal, QC, Canada

    Ahmad Maaref, Francisco Perdigon Romero, Emmanuel Montagnon, Milena Cerny, Geneviève Soucy, Simon Turcotte, An Tang & Samuel Kadoury

  3. Department of Pathology, Centre hospitalier de l’Université de Montréal (CHUM), Montreal, QC, Canada

    Bich Nguyen & Geneviève Soucy

  4. Department of Pathology and Cellular Biology, Université de Montréal, Montreal, QC, Canada

    Bich Nguyen & Geneviève Soucy

  5. Department of Surgery, Hepatopancreatobiliary and Liver Transplantation Service, Centre Hospitalier de l’Université de Montréal (CHUM), Montreal, QC, Canada

    Franck Vandenbroucke & Simon Turcotte

  6. Department of Radiology, Centre hospitalier de l’Université de Montréal (CHUM), Montreal, QC, Canada

    An Tang

Authors
  1. Ahmad Maaref
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco Perdigon Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emmanuel Montagnon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Milena Cerny
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bich Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Franck Vandenbroucke
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Geneviève Soucy
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Simon Turcotte
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. An Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Samuel Kadoury
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samuel Kadoury.

Ethics declarations

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (include name of committee + reference number) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was accepted and presented as an abstract for SIIM 2019, in Denver, CO.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maaref, A., Romero, F.P., Montagnon, E. et al. Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach. J Digit Imaging 33, 937–945 (2020). https://doi.org/10.1007/s10278-020-00332-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-020-00332-2

Keywords

Search

Navigation